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Article

Multivariate and Geometric Morphometrics Reveal Morphological Variation Among Sinibotia Fish

by
Yongming Wang
1,2,
Yong Xie
3,
Yanping Li
2,*,
Fei Peng
2,
Jinping Li
2,
Wei Jiang
3,
Biwen Xie
2,
Peng Fu
3,* and
Zuogang Peng
1,*
1
Key Laboratory of Freshwater Fish Reproduction and Development, Ministry of Education, School of Life Sciences, Southwest University, Chongqing 400715, China
2
Fishes Conservation and Utilization in the Upper Reaches of the Yangtze River Key Laboratory of Sichuan Province, College of Life Science, Neijiang Normal University, Neijiang 641100, China
3
Chongqing Fisheries Science Research Institute, Chongqing 400020, China
*
Authors to whom correspondence should be addressed.
Biology 2025, 14(9), 1177; https://doi.org/10.3390/biology14091177
Submission received: 29 July 2025 / Revised: 15 August 2025 / Accepted: 19 August 2025 / Published: 2 September 2025
(This article belongs to the Section Zoology)
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Simple Summary

Sinibotia is a typical representative of the tetraploid lineage of the Botiinae subfamily and is under study to clarify its taxonomic status and phylogenetic relationships. Elucidating phenotypic characteristics is essential for understanding species divergence. In this study, multivariate and geometric morphometric approaches were used to systematically analyze the morphological variations among five Sinibotia species. Both methods effectively distinguished between the Sinibotia species and yielded highly consistent results. This study emphasizes the effectiveness of the combined use of both methods to obtain comprehensive insights into the morphological differentiation among Sinibotia species and their close relationship with ecological adaptation.

Abstract

Sinibotia species, investigated for morphology and species divergence owing to comparable body patterns and frequent sympatric occurrences, show high morphological similarity and close phylogenetic relationships, which challenge their accurate distinguishing via conventional morphological methods. Hence, multivariate morphometric (MM) and geometric morphometric (GM) analyses were used to assess the morphological differences between Sinibotia species (S. superciliaris, S. reevesae, S. robusta, S. pulchra, and S. zebra) habiting the Tuo River (Zizhong County) and Li and Lipu Rivers (Pingle County) based on 40 morphological traits and 34 landmarks. The morphological traits of S. robusta contrasted with those of S. pulchra and S. zebra, whereas S. superciliaris and S. reevesae showed similar morphologies, consistent with the cluster results. MM analysis using discriminant function analysis along with GM methods such as canonical variate analysis and relative distortion analysis enabled the differentiation between the Sinibotia species. Morphological variations were primarily reflected in snout length, nasal snout distance, head depth, body depth, caudal fin length, and dorsal fin length. MM effectively quantified linear size differences, whereas GM better captured and visualized complex variations in overall shape. The combined morphological evidence presented in this study contributes significantly to the identification of species, phylogenetic relationships, and ecological adaptations of Sinibotia species, thereby strengthening the theoretical rationale for the conservation and sustainable utilization of this genus.

Graphical Abstract

1. Introduction

Freshwater fish account for approximately 40% of the global fish population and are important components of freshwater ecosystems [1]. Additionally, they are a significant source of protein for humans and are vital to the economy in many countries [2,3]. However, phenomena such as water environment deterioration [4,5], habitat loss [6], overfishing [7,8], biological invasion because of human activities [9,10,11], and global changes [12] pose serious threats to the survival of freshwater fish. The continued increase in the human population threatens the depletion of freshwater fish resources [13]. Consequently, the accurate assessment of resource quantity is the basis for the sustainable utilization of fish resources [14], and the scientific assessment of the stock quantity of various fish depends on the correct identification of species and the effective distinguishing of populations [15,16]. Phenotypic characteristics are important for recognizing organisms and establishing biological classification systems [17]. The response and adaptation of each species to the living environment determine its role and function in the ecosystem [18] and are closely related to the risk of species extinction [19,20]. Therefore, investigating and quantifying the information pertaining to phenotypic characteristics is of great significance for species identification and elucidating the adaptive evolution of freshwater fishes [21,22].
Morphometrics effectively determines the shape and size of organisms, making species identification taxonomically significant [17,23]. Morphometrics rather than molecular markers are widely used in the identification of fish populations because of factors such as low cost, easy operation, and minimal interference from equipment [24,25]. Currently, morphometric studies on species and identification of morphological variation in populations focus primarily on multivariate morphometrics (MM) and geometric morphometrics (GM). MM is a classic method used in fish fauna surveys. It is characterized by strong data traceability and has been widely used to study intra- and interspecific morphological changes in fishery resource management [26,27]. However, MM is based on the linear distance between two points; hence, the spatial pattern of morphological changes is not satisfactorily reflected, which limits its applicability [28]. GM addresses the shortcomings, such as different data sources, non-repeatability, and a lack of separate discussion of size and shape [29,30], and visualizes morphological differences, enables asymmetry assessment, and identifies allometric growth between complex shapes, which are crucial in resolving various issues related to morphological variation [31,32].
Botiidae (Actinopterygii: Cypriniformes) comprises a group of subjectively beautiful small- and medium-sized bottom-dwelling fish that are widely distributed in East, Southeast, and South Asia [33]. These species are of high ornamental and culinary value. The long-term separation between diploid (Leptobotiinae) and tetraploid (Botiinae) lineages of Botiidae presents an excellent model for examining the mechanisms underlying fish polyploidization [34]. The Sinibotia genus is a typical representative of the tetraploid lineage of Botiinae and has been subjected to several studies because of its taxonomic status and phylogenetic relationships [35,36,37,38]. Additionally, it is a valuable resource for investigating morphological adaptations and species divergence and gaining insights into patterns of coexistence and interspecific variation owing to the similarity of the Sinibotia species with respect to body surface patterns (characterized by broad dark brown bars that extend from one side of the body across the dorsal region to the opposite side and consistently extend below the lateral midline, typically reaching the level of the pelvic fin origin) [38,39] and widespread co-occurrence within the same habitat [38,40].
The recent rapid development of inland fisheries has led to the severe degradation of freshwater fish resources [41,42], and the concurrent commercial spread of these fisheries has led to frequent instances of the appearance of native species in non-native habitats, which poses a challenge for the conservation of Sinibotia species [43]. Hence, morphological variations among Sinibotia fish species need to be differentiated effectively using comprehensive morphometrics and multivariate statistical analyses. Therefore, the major objectives of this study were: (1) to determine morphometric differentiations using anatomic coordinates and landmark points among five Sinibotia species that show similar morphology and close relationships; (2) to provide a geometric topology-based quantitative discriminative system for assessing five Sinibotia germplasm resources; and (3) to compare the abilities of MM and GM analyses to capture and distinguish morphological variations in Sinibotia species. The overall aim was to provide supplementary information for fishery biology research on Sinibotia that could additionally act as foundational data for the assessment and conservation of fishery resources.

2. Materials and Methods

2.1. Sample Collection

A collection of fish species was performed in August during the years 2017–2023. In total, 150 individuals belonging to five Sinibotia species, namely, S. superciliaris, S. reevesae, S. robusta, S. pulchra, and S. zebra, were collected. Specifically, S. superciliaris and S. reevesae were obtained from the Tuo River in Zizhong County, S. zebra was collected from the Lipu River in Pingle County, and the other two species were collected from the Lijiang River in Pingle County (Figure 1). The specimens were euthanized using MS-222 (Sinopharm Chemical Reagent Co., Ltd, Shanghai, China), weighed, and placed in 95% ethanol (Sinopharm Chemical Reagent Co., Ltd, Shanghai, China) for initial fixation. Subsequently, they were positioned on their left side in a lateral orientation on a Styrofoam plate for photographing using a Canon EOS Kiss X 7 digital camera (Canon, Tokyo, Japan) to obtain a reference scale for standardization during post-processing. During photography, the fish morphology was restored to its natural state and secured using forceps and pins. The camera was mounted on a QH-C082 copystand (Wenzhou Changcheng Movie & TV Equipment Co., Ltd, Wenzhou, China) to maintain the lens parallel to the imaging plane of the specimen. The shooting distance was fixed and maintained at 300 mm to ensure a consistent scale among different samples and reduce the distortion caused by parallax during projection [44]. All procedures were performed by the same researcher to minimize human-induced errors. The specimens were stored in 95% ethanol and deposited at the Fish Conservation and Utilization in the Upper Reaches of the Yangtze River Key Laboratory of Sichuan Province, Neijiang Normal University College of Life Science (Neijiang, China). The samples and morphometric data used in this study are listed in Table 1.

2.2. Multivariate Morphometric Analyses

Morphological measurements of 16 traditional morphological traits, such as anal fin length (AFL), body depth (BD), caudal fin length (CFL), caudal peduncle length (CPL), caudal peduncle depth (CPD), dorsal fin length (DFL), eye diameter (ED), head length behind eyes (HBE), head depth (HD), head length (HL), nasal snout distance (nsd), pectoral fin length (PFL), standard length (SL), snout length (SnL), total length (TL), ventral fin length (VFL) were obtained from the photographs of the 150 Sinibotia specimens (Figure 2a). Additionally, 10 anatomical coordinates were chosen as truss network traits with the intercoordinate distances designated as D1–2 and D2–3 (Figure 2b) according to the methodology proposed by Xu et al. [40]. Thus, we obtained 24 comprehensive measurement metrics.
The measurements were captured in pixels using ImageJ (version 1.51j8) software and later transformed into millimeters (accurate to 0.01 mm) by referencing the scale of each image. To minimize measurement errors, two measurements were performed for each sample at different times. All morphological data were standardized by dividing them by the standard length to reduce the influence of length, followed by excluding the standard-length variable [24]. One-way analysis of variance (ANOVA) was used to detect significant differences between all measurements among the Sinibotia species, followed by Tukey’s post-hoc test to identify specific differences among these specimens. The values were expressed as mean ± SD. Then, the standardized morphological traits were analyzed using principal component analysis (PCA), discriminant function analysis (DFA), and cluster analysis (CA) using the PAST (version 4.09) software. PCA was performed to decrease data dimensionality and minimize redundancy among specimens. Moreover, the principal components (PCs) were used to build models, and their loadings were extracted for further analysis. DFA was performed to evaluate the accuracy of individual classifications within each population and calculate the success rate of the classification. A stepwise approach was used to identify key features that showed a significant impact. CA was used to analyze multivariate data to group similar populations into common clusters. In fact, a hierarchical cluster analysis was performed based on the Euclidean algorithm (repeated 10,000 times) of morphometric measurements among populations.

2.3. Geometric Morphometric Analyses

The external morphological characteristics of the genus Sinibotia were used to select 15 homologous landmarks from the lateral view of the body [32], as shown in Figure 3. To precisely delineate the outline of the study subject, 19 semi-landmarks (S1–S19, Figure 3) were generated on the lateral aspect of the body using the sliding technique with the MakeFan (version 8) software from the IMP package. Briefly, three equidistant vertical lines were drawn between each of the remaining pairs of points, with the exception of the segment between points 6 and 7, which was bisected with a single vertical line. The intersection points between the equidistant lines and the lateral profile of the fish acted as semi-landmarks.
The sample photographs were digitally processed using TPS series (tpsUtil version 1.60, tpsDig2 version 2.18, tpsSuper version 2.01) software [32]. The landmarks on the lateral side were arranged in order, starting from the tip of the snout and proceeding clockwise until the intersection point where the end of the occipital bone aligns perpendicularly to the ventral aspect. Subsequently, the landmarks were identified at the most anterior, dorsal, posterior, and ventral points of the orbit and posterior margin of the operculum to ensure consistency in sequence, number, and placement of markers on each image. All procedures were performed by the same researcher to minimize potential errors due to human variability.
After digitization, the data were imported into the MorphoJ (version 1.07) software for Generalized Procrustes Analysis (GPA) [45] to eliminate the effects of non-morphological variations caused by changes in position, orientation, and scale during the selection of landmark points. This is to ensure optimal results for subsequent statistical analysis. PCA was used to assess the geometric variation across samples. Canonical variate analysis (CVA) was used to assess morphological variations across Sinibotia populations, whereas DFA was used to compare morphological differences within pairwise populations. Quantitative analysis of morphological differences between pairs of populations was performed using Mahalanobis distance, followed by 10,000 repetitions of p-value testing. The Mahalanobis distance quantifies the covariance distance in the data, which aids in assessing the dissimilarity of an individual from other individuals in the sample and indicates the extent of overlap in inter-individual morphological variation. Additionally, the TPS files with the landmark information for each group were processed using tpsSuper (version 2.01) to derive the average shapes of each group, which were combined using tpsUtil (version 1.60) and imported into the PAST (version 4.09) software for cluster analysis based on the Euclidean algorithm (repeated 10,000 times) to elucidate the variations among all PCs.

3. Results

3.1. Morphological Variation Based on Multivariate Morphological Metrics

After transformation, the correlation coefficient between the standard length and 39 size-independent morphometric characteristics notably decreased. Initial data indicated correlation coefficients ranging from 0.336–0.804; the most transformed values showed correlations of <0.6 after adjustment, with the exception of D2–9. Among the specimens, S. reevesae was the longest, followed by S. superciliaris, whereas S. zebra was the shortest. Subsequent multivariate analysis showed that the ratio of specimen number to morphometric variables (N:P) was 3.85 [46].
One-way ANOVA showed significant variations in all 39 morphometric variables in all five Sinibotia species (p < 0.05, Table S1). As indicated in Table 2, with the exception of head length behind eyes (HBE), dorsal fin length (DFL), anal fin length (AFL), D2–8, D2–9, D4–5, D4–6, D4–10, and D5–6, the remaining 30 morphological indices exhibited two extremes in both the S. robusta and S. zebra specimens. Specifically, S. robusta showed the highest values for eye diameter (ED), head depth (HD), ventral fin length (VFL), caudal peduncle length (CFL), total length (TL), D1–2, D2–10, D3–4, D3–7, D3–8, D4–9, D7–8, and D8–9 (p < 0.05), whereas S. zebra showed the highest values for caudal peduncle length (CPL), D2–3, D3–10, D5–7, D6–7, and D9–10 (p < 0.05). S. pulchra showed the largest HBE and D4–5 (p < 0.05), whereas S. superciliaris showed similarities to S. reevesae. Significant differences (p < 0.05) were noted in various morphological parameters, such as snout length (SnL), nasal snout distance (NSD), HD, body depth (BD), AFL, CFL, DFL, caudal peduncle depth (CPD), TL, D2–3, D2–8, D2–9, D3–4, D3–8, D3–9, D3–10, D4–5, D4–8, D4–9, D4–10, D5–6, D5–8, and D9–10.
PCA identified six components with eigenvalues >1 from the morphometric measurements, which accounted for 82.10% of the variations among the five Sinibotia fish species (Figure S1). Specifically, the first and second components contributed 45.22% and 13.84% of the variation, respectively. The PCA scatter plot (Figure 4a) showed that S. zebra exhibited significant morphological differences from those of the other four species and could be completely separated using the anatomical landmarks chosen in this study. In contrast, S. superciliaris shares morphological similarities with S. reevesae, S. pulchra, and S. robusta and cannot be entirely distinguished through PCA analysis. Furthermore, DFA based on MM analysis showed effective differentiation between S. zebra, S. pulchra, and S. robusta, whereas S. superciliaris and S. reevesae were difficult to distinguish and showed morphological overlaps (Figure 4b).
The three PCs with the highest contribution rates were selected for the factor analysis (Table 3). PC1 showed strong correlations with most morphological variables, with the exception of HBE, NSD, D1–2, D2–8, D2–9, D3–10, D4–6, D4–10, D5–8, and D9–10. The most robust correlations were observed with indices related to body depth (BD, D3–7, D3–8, D3–9, and D4–9). PC2 exhibited a negative correlation with D1–2 and a positive correlation with all other variables, with strong correlations observed with HL, CFL, TL, D1–2, D2–3, D2–8, D2–9, D3–10, and D9–10. PC3 primarily correlated with DFL, AFL, and D4–10.
Further analysis showed that BD, CFL, DFL, HBE, HD, PFL, SnL, D3–4, D4–5, D4–9, D4–10, D5–6, and D5–7 exhibited the highest loadings among the 39 morphological traits and significantly influenced the discriminant analysis. The classification function coefficients for each population based on these key variables are listed in Table 4. Furthermore, stepwise discriminant analysis showed 100% comprehensive discriminant rates and cross-validation rates for each group (Table S2), which indicates a precise discrimination of the five Sinibotia species.
Euclidean distances were calculated for the hierarchical cluster analysis using the average value of each morphometric measurement for all five species. As shown in Figure 5, categorization of the five Sinibotia species showed two primary clades: one comprising S. pulchra and S. zebra, and the other formed by the initial clustering of S. superciliaris with S. reevesae, followed by their subsequent grouping with S. robusta.

3.2. Morphological Variation Based on Geometric Morphometrics

The Sinibotia species dataset, which comprised 34 landmarks in lateral view, showed that PC1 and PC2 explained 56.96% and 14.40% of the variance, respectively (Figure 6a). The primary factors that influenced trunk height variation were landmarks 3 and 9, along with the semi-landmarks S6 and S14, which were associated with PC1. In contrast, landmarks 2 and 5, which were linked to PC2, primarily explained the variability in head height. The PCA of 34 landmark points in the lateral view showed a partial morphological overlap between S. pulchra and S. zebra; however, they could be distinguished from S. superciliaris, S. reevesae, and S. robusta. S. superciliaris and S. reevesae showed significant morphological overlap with each other, whereas S. robusta exhibited minor overlap with S. superciliaris and S. reevesae.
CVA showed that CV1 and CV2 represented 64.00% and 26.79% of the total variance, respectively (Figure 6b). The scatter plot graph with CV1 and CV2 as the X and Y axes showed a distinct separation among S. robusta populations with 95% confidence. In contrast, S. pulchra and S. zebra populations were in close proximity, and some overlap was noted between the S. superciliaris and S. reevesae populations. Analysis of the CV1 axis showed that S. pulchra and S. zebra were situated on the negative side of the axis, whereas S. robusta predominantly occupied the positive side. S. superciliaris and S. reevesae were positioned at the center of the axis. The morphological variations that showed positive values on the CV1 axis included shortened head, enlarged eyes, increased trunk height, and shortened yet heightened tail (Figure 6b). Analysis of the CV2 axis showed that S. robusta, S. pulchra, and S. zebra were distributed along the negative axis, whereas S. superciliaris and S. reevesae were positioned along the positive axis (Figure 6b). Morphological alterations that showed positive values on the CV2 axis were shortened and reduced head, decreased eye size, elongated trunk, and shortened tail (Figure 6b).
The DFA based on 34 landmarks of the Sinibotia species corroborated the findings of PCA and CVA, which highlight the distinctiveness of S. robusta. The DFA showed that S. robusta proximity was between S. pulchra and S. zebra, and it overlapped between S. superciliaris and S. reevesae (Figure S2). DFA was performed on pairwise combinations of the five Sinibotia species to determine the phenotypic differentiation trends between the populations. According to the Mahalanobis distance results (Figure 7), the morphological differences were highly significant among the Sinibotia populations (p < 0.001), and all groups achieved a 100% discriminant rate. Among them, S. robusta showed the largest Mahalanobis distance compared with those of the other populations, particularly S. zebra (21.6623). In contrast, S. pulchra exhibited a relatively small Mahalanobis distance from S. zebra, whereas S. superciliaris and S. reevesae showed the shortest distance between them (6.7297). These results emphasize the significant morphological variation between S. robusta and S. zebra, whereas S. superciliaris and S. reevesae showed the least variability.
The thin-plate spline and warped outline drawings included in Figure 7 show notable morphological disparities in the head, trunk, and tail regions of the five Sinibotia species. Observation of head morphology showed that S. zebra exhibited the shortest head length, and S. robusta and S. pulchra showed the longest. S. superciliaris and S. reevesae showed intermediate lengths. Notably, S. robusta exhibited the tallest head, followed by S. reevesae, whereas S. superciliaris and S. pulchra showed similar head heights, and S. zebra showed the shortest head height. Additionally, S. robusta exhibited the largest eyes, whereas S. zebra showed the smallest eyes. The snouts showed similar positioning in S. superciliaris and S. reevesae, although they were notably elevated compared to those of the other Sinibotia species. Study of trunk morphology showed that trunk height varied in the following order: S. robusta > S. reevesae > S. superciliaris > S. pulchra > S. zebra, which was the smallest in stature. Analysis of the tail region showed that S. pulchra and S. zebra showed the longest and shortest caudal peduncles, respectively. The other three Sinibotia species exhibited similar caudal peduncle lengths, with S. reevesae and S. robusta having comparable and relatively tall caudal peduncle lengths.
Based on the CA, the five Sinibotia species were generally categorized into two groups: S. pulchra and S. zebra were grouped together, whereas S. superciliaris was first placed with S. reevesae, and both were placed with S. robusta (Figure S3). This result was consistent with the results of the MM analysis of the 39 morphometric variations.

4. Discussion

4.1. Benefits of Combining Multivariate and Geometric Morphometrics in Species Identification

The identification of species and their stocks within natural populations is essential for estimating global fish diversity and is crucial for open-water fishery management and the sustainable use of fish species for human welfare [47,48,49]. Although genetic tools are highly effective and widely used in the conservation and management of fishery resources [50], an excessive genetic focus may detract from the goals of taxonomy, systematics, and population characterization. This highlights the need for a holistic approach [51]. MM and GM are techniques that are commonly used for species identification—particularly for fish classification [52], individual ontogeny [53], population subdivision [54], and physiological ecology [55]. These distinct morphological measurement approaches often emphasize the different dimensions of morphological variation among individuals or taxonomic groups; thus, they capture diverse aspects of morphology, which provide varying interpretations of morphological differences in fish [56]. Hence, the integration of diverse morphometric measurement techniques is essential to achieve a comprehensive analysis of the morphological variations in fish species or populations. Studies similar to the current one have been conducted on the morphological identification of the Chimarrichthys fish complex [57], coral reef fish [56], and sardines [58].
This present study integrated MM and GM to hierarchically investigate the morphological differences among five Sinibotia species. Both methods yielded results that indicated that the morphological differences among the Sinibotia species phenotypes were primarily concentrated in head length and depth, eye size, trunk depth, tail length, and height. Additionally, multivariate statistical analyses consistently indicated the relative independence of S. robusta, whereas S. pulchra and S. zebra were grouped together, owing to their similar morphologies. S. superciliaris and S. reevesae exhibited minimal morphological distinctions and certain overlap. These results are consistent with those of previous studies on the phylogeny of botiid loaches using mitochondrial and nuclear genes [59], which validates the efficacy of both approaches in differentiating Sinibotia species. Furthermore, this study has highlighted two distinct strategies that have led to slightly varying interpretations of the morphological differences in Sinibotia species in fin and body morphology. GM effectively captured shape information from homologous landmarks by visually interpreting the morphological and size differences among the five Sinibotia species; however, MM methods provided a more accurate quantification of the differences in fish body shape. Hence, morphological analysis using GM methods needs to be considered in tandem with MM analysis, and the essential contribution of MM should not be dismissed when using GM for morphological analysis.

4.2. Morphological Variation Among Sinibotia Species

The morphological structure of an organism is the result of the prolonged interplay of genetic information and environmental factors [60], and it not only acts as a primary basis for species identification and classification [61], but also plays a crucial role in the study of biological evolution [62], adaptive mechanisms [63], and functional ecology [64]. The Sinibotia genus was first established by Fang in 1936 [65], and it includes multiple species that have been described and documented to date [39]. The predominant distribution of Sinibotia in biodiversity hotspots in East Asia necessitates its accurate quantification and thorough understanding of its morphological variations, not only to form the basis of taxonomic and systematic studies, but also to represent a critical pathway for identifying ecological adaptations, evolutionary processes, and functional trade-offs.
Morphological studies on Sinibotia fish have been limited to basic descriptions of certain species. Bohlen et al. [38] conducted a comparative study of 33 morphological characteristics and reported that S. zebra showed a morphology that was intermediate to those of Leptobotia guilinensis and S. pulchra. They noted a resemblance between the head stripes of S. zebra and S. pulchra, whereas the body marking pattern closely resembled that of L. guilinensis. Xu et al. [40] compared 10 traditional morphological parameters and 20 truss network features of S. superciliaris and S. reevesae and found that PCA could not differentiate between the two species. Wu et al. [39] have indicated that snout length, eye diameter, body depth, suborbital spine shape, and body coloration are important criteria for classifying and identifying species in the Sinibotia genus. Therefore, by combining the MM and GM methods, the present study has identified more diverse morphological differences than those reported by previous studies. Based on the overall findings, the five Sinibotia species exhibited significant differences in head length (SnL and HBE), HD, ED, HD, CPL, CPD, and fin morphology (CFL, DFL, and PFL). The most pronounced differences in morphology were observed between S. robusta and S. zebra, which showed substantial variations in multiple morphological indicators. In contrast, the morphological differences between S. superciliaris and S. reevesae were the least significant. The morphological variations among the five Sinibotia species were as follows: S. robusta exhibited the highest HD, ED, CFL, D3–4, and D4–9; S. pulchra showed the highest HBE and D4–5; and S. zebra showed the highest CPL and D5–7. Notable differences between S. superciliaris and S. reevesae included variation in SnL, BD, CFL, DFL, CPD, D3–4, D4–9, D4–10, and D5–6.

4.3. Morphological Variation and Ecological Adaptation Among Sinibotia Species

Fish form the largest group of vertebrate species; hence, the diversity in fish morphology is the result of long-term adaptation to complex and ever-changing aquatic environments. Incidentally, this highlights the significance of natural selection in shaping biological phenotypes [66]. The common ancestor of the Sinibotia genus likely began to diverge in the Early Miocene (21.1 Mya) and spread from the mainland of Southeast Asia to East Asia [67]. The geological movements in East Asia since the Miocene period (such as the uplift of the Qinghai–Tibet Plateau) and changes in water systems have enabled the species to develop distribution patterns that are suitable for habitation in the Pearl River (S. robusta, S. pulchra, and S. zebra) and Yangtze River systems (S. superciliaris and S. reevesae) [59]. Investigating the morphological differentiation and speciation of Sinibotia in allopatric or sympatric regions could provide insights into the mechanisms that maintain species diversity and provide additional theoretical guidance for the sustainable utilization of fishery resources and species conservation.
Sinibotia fish in the Pearl River system showed three morphs: S. pulchra showed similarity to S. zebra, and S. robusta was distinct. In contrast, the Sinibotia fish in the Yangtze River system showed two morphs that fell between the three morphs found in the Pearl River system. Both ancient connections and significant genetic differentiation are known to exist among the fish populations in the Yangtze and Pearl River systems, which aligns with the speculated ancient hydrological evolution history [68]. This leads to the speculation that the ancestors of the Sinibotia genus were the first to enter the Pearl River system but later became isolated because of the evolution of the water system (such as river capture, channel changes, and ancient water system fragmentation) and glacial–interglacial cycles, leading to gradual differentiation into different species under different selection pressures and genetic drift [69,70,71].
The significant variation in morphology among the three Sinibotia species found in the Pearl River system could be attributed to the distinct geographic, topographic, and climatic conditions of the basin, which offer a diverse range of ecological niches for fish within the basin [72]; furthermore, this could have influenced the adaptive evolution of their morphology. S. robusta exhibits characteristics that show adaptation to slow-flowing or still water [73]. The larger eyes are conducive to the quick spotting of prey and predators [74], and its robust body and long pectoral, pelvic, and caudal fins ensure high maneuverability, which is essential for flexibility in swimming in complex environments, maintaining stability, and rapid escape [75,76]. S. pulchra and S. zebra possess small eyes, slender bodies, narrow caudal peduncles, and short fins, which help reduce resistance during swimming and are suitable for fast and sustained swimming or living in swift water currents [77]. Furthermore, despite their notable morphological similarities, S. superciliaris and S. reevesae did display variations that may be attributed to habitat utilization. For example, the body shape of S. superciliaris is slender, whereas that of S. reevesae is robust. This indicates a strong affinity for interspecific hybridization between S. superciliaris and S. reevesae [78]. However, recent overfishing [79], increased water pollution [80], and hydraulic engineering [81] have significantly increased the competition for limited resources and potential hybridization risk between S. superciliaris and S. reevesae, which exist under the same habitat conditions, posing challenges for the conservation of the species. Thus, further study of the morphological variance among closely related species of the Sinibotia genus, with a specific focus on the interaction of multiple ecological factors, phenotypic plasticity, and mechanisms underlying morphological differentiation and species formation, is crucial for enhancing our understanding of the ecological adaptability and morphological differentiation patterns of Sinibotia species. Furthermore, this study provides essential theoretical evidence for the conservation and sustainable utilization of aquatic biodiversity.

5. Conclusions

This study has successfully identified significant morphological differences among five Sinibotia species through the integrated application of multivariate and geometric morphometrics. These differences were primarily observed in head length, head depth, body depth, caudal peduncle length, and caudal peduncle depth. These findings not only provide important morphological evidence for species identification and phylogenetic relationships within the Sinibotia genus, but also offer a new perspective for understanding its underlying adaptive evolutionary mechanisms. Furthermore, to address the high conservation pressures for Sinibotia species, future studies should explore a broader geographical spectrum of morphological variations by incorporating osteology, comparative genomics, and epigenetics to comprehensively elucidate the molecular mechanisms underlying speciation and adaptive evolution of the Sinibotia species.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology14091177/s1, Table S1: ANOVA table for multivariate morphological characters among Sinibotia fishes; Table S2: Results of discriminant function analysis based on multivariate morphological characters; Figure S1: Scree plot of the PCA based on multivariate morphological characters; Figure S2: Relative distortion analysis within the Sinibotia species; Figure S3: Dendrogram derived based on 34 landmarks of the lateral view.

Author Contributions

Conceptualization, Z.P. and Y.W.; investigation, Y.W., Y.X., B.X. and W.J.; methodology, Y.W., Y.X., Y.L., B.X. and W.J.; formal analysis, Y.W., F.P., J.L. and Y.X.; visualization, Y.W., Y.X., Y.L. and F.P.; supervision, P.F. and Z.P.; writing—original draft, Y.W.; writing—review and editing, Z.P. and Y.L.; project administration, P.F.; funding acquisition, Y.W., Y.L. and P.F. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by Special Program for Performance Incentives Guidance in Research Institutions in Chongqing Municipality (no. cstc2022jxjl80009); Innovative Research Team in Neijiang Normal University (no. 2021TD03); Sichuan Science and Technology Program (no. 2025ZNSFSC1077); The Major Project in Neijiang Normal University (no. 2024ZDZ05) and Water Resources Bureau of Chongqing Municipal (no. CQS23C01036).

Institutional Review Board Statement

The animal study protocol was approved by the Ethics Committee of Neijiang Normal University (protocol code: NJNU-KJC-2025006).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of sampling sites of Sinibotia fishes. S. superciliaris and S. reevesae were collected from the Tuo River in Zizhong County in August 2017. The other three species were collected from the Li and Lipu Rivers in Pingle County in August 2023.
Figure 1. Map of sampling sites of Sinibotia fishes. S. superciliaris and S. reevesae were collected from the Tuo River in Zizhong County in August 2017. The other three species were collected from the Li and Lipu Rivers in Pingle County in August 2023.
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Figure 2. Multivariate morphometric metrics measured. (a). Initially, 16 traditional morphological characteristics were measured: AFL, anal fin length; BD, body depth; CFL, caudal fin length; CPL, caudal peduncle length; CPD, caudal peduncle depth; DFL, dorsal fin length; ED, eye diameter; HBE, head length behind eyes; HD, head depth; HL, head length; NSD, nasal snout distance; PFL, pectoral fin length; SL, standard length; SnL, snout length; TL, total length; VFL, ventral fin length; (b). Additionally, 24 variables were extracted from 10 anatomic coordinates: 1. tip of snout; 2. end of occipital bone; 3. origin of dorsal fin base; 4. end of dorsal fin base; 5. dorsal origin of caudal fin base; 6. ventral origin of caudal fin base; 7. end of anal fin base; 8. origin of anal fin base; 9. origin of pelvic fin base; and 10. origin of pectoral fin base; D1–2 denotes the distance between anatomic coordinates 1 and 2.
Figure 2. Multivariate morphometric metrics measured. (a). Initially, 16 traditional morphological characteristics were measured: AFL, anal fin length; BD, body depth; CFL, caudal fin length; CPL, caudal peduncle length; CPD, caudal peduncle depth; DFL, dorsal fin length; ED, eye diameter; HBE, head length behind eyes; HD, head depth; HL, head length; NSD, nasal snout distance; PFL, pectoral fin length; SL, standard length; SnL, snout length; TL, total length; VFL, ventral fin length; (b). Additionally, 24 variables were extracted from 10 anatomic coordinates: 1. tip of snout; 2. end of occipital bone; 3. origin of dorsal fin base; 4. end of dorsal fin base; 5. dorsal origin of caudal fin base; 6. ventral origin of caudal fin base; 7. end of anal fin base; 8. origin of anal fin base; 9. origin of pelvic fin base; and 10. origin of pectoral fin base; D1–2 denotes the distance between anatomic coordinates 1 and 2.
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Figure 3. Landmarks and semi-landmarks used for geometric morphometric analysis. 1. tip of snout; 2. end of occipital bone; 3. origin of dorsal fin base; 4. end of dorsal fin base; 5. dorsal origin of caudal fin base; 6. ventral origin of caudal fin base; 7. end of anal fin base; 8. origin of anal fin base; 9. origin of pelvic fin base; 10. point of intersection of the end of the occipital bone perpendicular to the ventral aspect; 11. anterior-most point of orbit; 12. dorsal-most point of orbit; 13. posterior-most point of orbit; 14. ventral-most point of orbit; 15. posterior-most margin of operculum. S1–19 indicate the intersection points between the equidistant lines and the lateral profile of the fish. The red dots indicate homologous landmarks, and blue dots indicate semi-landmarks.
Figure 3. Landmarks and semi-landmarks used for geometric morphometric analysis. 1. tip of snout; 2. end of occipital bone; 3. origin of dorsal fin base; 4. end of dorsal fin base; 5. dorsal origin of caudal fin base; 6. ventral origin of caudal fin base; 7. end of anal fin base; 8. origin of anal fin base; 9. origin of pelvic fin base; 10. point of intersection of the end of the occipital bone perpendicular to the ventral aspect; 11. anterior-most point of orbit; 12. dorsal-most point of orbit; 13. posterior-most point of orbit; 14. ventral-most point of orbit; 15. posterior-most margin of operculum. S1–19 indicate the intersection points between the equidistant lines and the lateral profile of the fish. The red dots indicate homologous landmarks, and blue dots indicate semi-landmarks.
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Figure 4. Scatter plot for multivariate morphological characters of five Sinibotia species. (a) Result of principal component analysis from PC1 and PC2, and (b) discriminant function analysis plot with 39 morphometric variations.
Figure 4. Scatter plot for multivariate morphological characters of five Sinibotia species. (a) Result of principal component analysis from PC1 and PC2, and (b) discriminant function analysis plot with 39 morphometric variations.
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Biology 14 01177 g005
Figure 5. Dendrogram based on multivariate morphometric measurements.
Figure 5. Dendrogram based on multivariate morphometric measurements.
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Figure 6. Scatter plot of geometric morphometric analysis of five Sinibotia species. (a) Principal component analysis of PC1 and PC2, and (b) canonical variate analysis of the five Sinibotia species based on 34 landmarks on the lateral view. Circles in CVA indicate 95% confidence.
Figure 6. Scatter plot of geometric morphometric analysis of five Sinibotia species. (a) Principal component analysis of PC1 and PC2, and (b) canonical variate analysis of the five Sinibotia species based on 34 landmarks on the lateral view. Circles in CVA indicate 95% confidence.
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Biology 14 01177 g007
Figure 7. Relative distortion analysis of the Sinibotia species based on 34 landmarks on the lateral view. Discriminant scores (a) and warped outline drawings of the morphological structure (b) between populations. Discriminant function analyses results are indicated with Mahalanobis distances (MD) on the upper right of the discriminant score plots. Significance levels: ** p < 0.001.
Figure 7. Relative distortion analysis of the Sinibotia species based on 34 landmarks on the lateral view. Discriminant scores (a) and warped outline drawings of the morphological structure (b) between populations. Discriminant function analyses results are indicated with Mahalanobis distances (MD) on the upper right of the discriminant score plots. Significance levels: ** p < 0.001.
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Table 1. Sample and morphometric information of five Sinibotia fishes used in this study.
Table 1. Sample and morphometric information of five Sinibotia fishes used in this study.
SpeciesnSampling LocationStandard Length (mm)Total Weight (g)
RangeMean ± SDRangeMean ± SD
S. superciliaris30Zizhong, Tuo River76.95–81.3379.14 ± 5.876.87–7.967.41 ± 1.46
S. reevesae30Zizhong, Tuo River89.38–95.3992.38 ± 8.0512.64–15.2913.96 ± 3.54
S. robusta32Pingle, Li River69.06–74.4971.77 ± 7.547.57–9.228.40 ± 2.29
S. pulchra30Pingle, Li River73.04–77.2875.16 ± 5.694.78–5.515.14 ± 0.98
S. zebra28Pingle, Lipu River66.34–70.5768.46 ± 5.452.93–3.453.19 ± 0.67
Table 2. Description statistics for morphological characters of five species in Sinibotia (Mean ± SD).
Table 2. Description statistics for morphological characters of five species in Sinibotia (Mean ± SD).
VariableS. superciliarisS. reevesaeS. robustaS. pulchraS. zebra
HL0.269 ± 0.011 b0.263 ± 0.014 b0.281 ± 0.012 a0.278 ± 0.013 a0.245 ± 0.010 c
ED0.031 ± 0.006 bc0.036 ± 0.009 b0.053 ± 0.011 a0.029 ± 0.008 cd0.025 ± 0.009 d
SnL0.117 ± 0.010 a0.108 ± 0.013 b0.114 ± 0.009 ab0.111 ± 0.009 ab0.096 ± 0.007 c
HBE0.119 ± 0.009 b0.119 ± 0.009 bc0.113 ± 0.012 c0.137 ± 0.009 a0.124 ± 0.006 b
HD0.158 ± 0.009 c0.166 ± 0.008 b0.194 ± 0.012 a0.153 ± 0.012 c0.131 ± 0.012 d
NSD0.070 ± 0.008 a0.061 ± 0.009 b0.070 ± 0.009 a0.070 ± 0.009 a0.059 ± 0.007 b
BD0.215 ± 0.015 b0.237 ± 0.015 a0.241 ± 0.019 a0.166 ± 0.013 c0.140 ± 0.013 d
DFL0.164 ± 0.023 a0.147 ± 0.025 b0.139 ± 0.013 bc0.126 ± 0.013 c0.101 ± 0.010 d
PFL0.142 ± 0.024 ab0.135 ± 0.022 b0.152 ± 0.020 a0.103 ± 0.013 c0.066 ± 0.012 d
VFL0.121 ± 0.017 b0.119 ± 0.016 b0.134 ± 0.016 a0.104 ± 0.012 c0.077 ± 0.012 d
AFL0.146 ± 0.019 a0.132 ± 0.021 b0.133 ± 0.014 b0.111 ± 0.010 c0.088 ± 0.012 d
CPL0.152 ± 0.006 bc0.159 ± 0.010 b0.149 ± 0.013 c0.160 ± 0.013 b0.175 ± 0.019 a
CPH0.144 ± 0.007 b0.164 ± 0.010 a0.158 ± 0.011 a0.130 ± 0.009 c0.126 ± 0.012 c
CFL0.222 ± 0.026 b0.199 ± 0.032 c0.305 ± 0.027 a0.234 ± 0.012 b0.178 ± 0.014 d
TL1.221 ± 0.024 b1.197 ± 0.031 c1.289 ± 0.021 a1.230 ± 0.016 b1.172 ± 0.012 d
D1–20.222 ± 0.014 c0.218 ± 0.016 c0.254 ± 0.016 a0.239 ± 0.016 b0.219 ± 0.020 c
D1–100.272 ± 0.012 a0.262 ± 0.017 a0.272 ± 0.016 a0.272 ± 0.014 a0.243 ± 0.016 b
D2–30.336 ± 0.017 b0.361 ± 0.016 a0.303 ± 0.023 c0.330 ± 0.022 b0.368 ± 0.021 a
D2–80.598 ± 0.015 bc0.620 ± 0.023 a0.601 ± 0.021 b0.585 ± 0.019 c0.583 ± 0.023 c
D2–90.392 ± 0.017 b0.419 ± 0.017 a0.378 ± 0.016 c0.369 ± 0.014 c0.377 ± 0.017 c
D2–100.147 ± 0.011 b0.152 ± 0.013 b0.170 ± 0.017 a0.132 ± 0.012 c0.113 ± 0.011 d
D3–40.126 ± 0.009 c0.134 ± 0.011 b0.177 ± 0.011 a0.122 ± 0.009 c0.103 ± 0.011 d
D3–70.368 ± 0.015 b0.375 ± 0.016 b0.414 ± 0.015 a0.348 ± 0.012 c0.300 ± 0.016 d
D3–80.313 ± 0.023 c0.325 ± 0.013 b0.358 ± 0.015 a0.295 ± 0.014 d0.251 ± 0.014 e
D3–90.212 ± 0.016 b0.237 ± 0.016 a0.237 ± 0.018 a0.163 ± 0.016 c0.140 ± 0.013 d
D3–100.321 ± 0.016 b0.360 ± 0.02 a0.327 ± 0.014 b0.327 ± 0.016 b0.354 ± 0.012 a
D4–50.306 ± 0.014 b0.292 ± 0.012 c0.285 ± 0.018 c0.324 ± 0.013 a0.310 ± 0.015 b
D4–60.347 ± 0.014 ab0.349 ± 0.013 ab0.348 ± 0.013 ab0.356 ± 0.013 a0.343 ± 0.017 b
D4–70.257 ± 0.011 a0.257 ± 0.012 a0.261 ± 0.013 a0.231 ± 0.009 b0.204 ± 0.013 c
D4–80.217 ± 0.011 b0.227 ± 0.011 a0.232 ± 0.014 a0.190 ± 0.012 c0.169 ± 0.013 d
D4–90.218 ± 0.010 c0.244 ± 0.018 b0.256 ± 0.017 a0.179 ± 0.015 d0.169 ± 0.014 d
D4–100.423 ± 0.012 c0.466 ± 0.023 a0.467 ± 0.017 a0.433 ± 0.015 bc0.441 ± 0.011 b
D5–60.147 ± 0.007 c0.170 ± 0.011 a0.155 ± 0.010 b0.125 ± 0.011 d0.121 ± 0.009 d
D5–70.111 ± 0.013 c0.117 ± 0.011 c0.117 ± 0.014 c0.142 ± 0.013 b0.153 ± 0.013 a
D5–80.250 ± 0.012 b0.271 ± 0.011 a0.271 ± 0.015 a0.257 ± 0.016 b0.251 ± 0.012 b
D6–70.111 ± 0.013 c0.117 ± 0.011 c0.117 ± 0.014 c0.142 ± 0.013 b0.153 ± 0.008 a
D7–80.081 ± 0.008 b0.081 ± 0.009 b0.091 ± 0.008 a0.078 ± 0.010 b0.065 ± 0.008 c
D8–90.223 ± 0.017 b0.220 ± 0.018 bc0.244 ± 0.010 a0.225 ± 0.013 b0.212 ± 0.012 c
D9–100.292 ± 0.014 b0.322 ± 0.021 a0.285 ± 0.016 b0.297 ± 0.018 b0.316 ± 0.013 a
Note: AFL, anal fin length; BD, body depth; CFL, caudal fin length; CPL, caudal peduncle length; CPD, caudal peduncle depth; Di–j denotes the distance between anatomic coordinates i and j according to Figure 2b; DFL, dorsal fin length; ED, eye diameter; HBE, head length behind eyes; HD, head depth; HL, head length; NSD, nasal snout distance; PFL, pectoral fin length; SnL, snout length; TL, total length; VFL, ventral fin length; different letters in superscript indicate significant differences (p < 0.05).
Table 3. Loading of the first three components in PCA based on multivariate morphological characters.
Table 3. Loading of the first three components in PCA based on multivariate morphological characters.
VariablePrinciple ComponentVariablePrinciple Component
PC1PC2PC3PC1PC2PC3
HL0.620−0.508−0.050D2–100.858−0.007−0.029
ED0.675−0.0790.281D3–40.844−0.1750.328
SnL0.550−0.323−0.087D3–70.954−0.0510.092
HBE−0.404−0.304−0.256D3–80.9300.0100.083
HD0.876−0.1410.134D3–90.9090.3150.002
NSD0.317−0.437−0.055D3–10−0.2170.7090.390
BD0.9140.293−0.028D4–5−0.553−0.261−0.187
DFL0.5600.131−0.666D4–60.062−0.1990.125
PFL0.8180.043−0.378D4–70.8580.116−0.172
VFL0.781−0.011−0.332D4–80.8980.203−0.063
AFL0.6650.093−0.573D4–90.9000.2700.179
CPL−0.5850.0640.178D4–100.4410.4230.695
CPD0.7730.3810.067D5–60.7590.414−0.019
CFL0.699−0.5020.342D5–7−0.726−0.2030.371
TL0.713−0.5040.314D5–80.4250.1710.343
D1–20.404−0.6370.367D6–7−0.726−0.2030.371
D1–100.538−0.370−0.131D7–80.746−0.1580.088
D2–3−0.5120.704−0.008D8–90.521−0.2070.393
D2–80.4100.6300.164D9–10−0.3090.6620.228
D2–90.2830.828−0.095
Note: AFL, anal fin length; BD, body depth; CFL, caudal fin length; CPL, caudal peduncle length; CPD, caudal peduncle depth; Di–j denotes the distance between anatomic coordinates i and j according to Figure 2b; DFL, dorsal fin length; ED, eye diameter; HBE, head length behind eyes; HD, head depth; HL, head length; NSD, nasal snout distance; PFL, pectoral fin length; SnL, snout length; TL, total length; VFL, ventral fin length.
Table 4. Classification function coefficients of the Sinibotia species based on multivariate morphological characters.
Table 4. Classification function coefficients of the Sinibotia species based on multivariate morphological characters.
VariableS. superciliarisS. reevesaeS. robustaS. pulchraS. zebra
BD−649.906−652.521−593.096−842.953−992.365
CFL855.276740.2691032.533891.593681.982
DFL1936.751732.9451686.1951905.2291783.063
HBE1564.2291575.6581506.5931960.8711880.49
HD1235.4581267.2571753.9851485.9281320.554
PFL91.896135.38313.923−35.366−154.035
SnL3564.33229.943105.5823576.3563316.018
D3–4625.357564.0641202.921710.819411.203
D4–53524.5263330.8143443.5423590.3673329.115
D4–9−962.795−882.986−1146.886−1194.772−973.426
D4–103206.2053305.0723233.7793432.5473425.339
D5–6−412.321−76.278−458.97−701.59−532.633
D5–7−342.912−273.204−233.37−70.45568.72
Constent−1693.693−1669.88−1817.476−1813.591−1622.784
Note: BD, body depth; CFL, caudal fin length; Di–j denotes the distance between anatomic coordinates i and j according to Figure 2b; DFL, dorsal fin length; HBE, head length behind eyes; HD, head depth; PFL, pectoral fin length; SnL, snout length.
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Wang, Y.; Xie, Y.; Li, Y.; Peng, F.; Li, J.; Jiang, W.; Xie, B.; Fu, P.; Peng, Z. Multivariate and Geometric Morphometrics Reveal Morphological Variation Among Sinibotia Fish. Biology 2025, 14, 1177. https://doi.org/10.3390/biology14091177

AMA Style

Wang Y, Xie Y, Li Y, Peng F, Li J, Jiang W, Xie B, Fu P, Peng Z. Multivariate and Geometric Morphometrics Reveal Morphological Variation Among Sinibotia Fish. Biology. 2025; 14(9):1177. https://doi.org/10.3390/biology14091177

Chicago/Turabian Style

Wang, Yongming, Yong Xie, Yanping Li, Fei Peng, Jinping Li, Wei Jiang, Biwen Xie, Peng Fu, and Zuogang Peng. 2025. "Multivariate and Geometric Morphometrics Reveal Morphological Variation Among Sinibotia Fish" Biology 14, no. 9: 1177. https://doi.org/10.3390/biology14091177

APA Style

Wang, Y., Xie, Y., Li, Y., Peng, F., Li, J., Jiang, W., Xie, B., Fu, P., & Peng, Z. (2025). Multivariate and Geometric Morphometrics Reveal Morphological Variation Among Sinibotia Fish. Biology, 14(9), 1177. https://doi.org/10.3390/biology14091177

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